logo EDITE Arthur BERNARD
Identité
Arthur BERNARD
État académique
Thèse soutenue le 2016-11-28
Sujet: Evolution de la coopération mutualiste pour la robotique collective
Direction de thèse:
Laboratoire:
Voisinage
Ellipse bleue: doctorant, ellipse jaune: docteur, rectangle vert: permanent, rectangle jaune: HDR. Trait vert: encadrant de thèse, trait bleu: directeur de thèse, pointillé: jury d'évaluation à mi-parcours ou jury de thèse.
Productions scientifiques
oai:hal.archives-ouvertes.fr:hal-01314721
To Cooperate or Not to Cooperate: Why Behavioural Mechanisms Matter
International audience
Mutualistic cooperation often requires multiple individuals to behave in a coordinated fashion. Hence, while the evolutionary stability of mutualistic cooperation poses no particular theoretical difficulty, its evolutionary emergence faces a chicken and egg problem: an individual cannot benefit from cooperating unless other individuals already do so. Here, we use evolutionary robotic simulations to study the consequences of this problem for the evolution of cooperation. In contrast with standard game-theoretic results, we find that the transition from solitary to cooperative strategies is very unlikely, whether interacting individuals are genetically related (cooperation evolves in 20% of all simulations) or unrelated (only 3% of all simulations). We also observe that successful cooperation between individuals requires the evolution of a specific and rather complex behaviour. This behavioural complexity creates a large fitness valley between solitary and cooperative strategies, making the evolutionary transition difficult. These results reveal the need for research on biological mechanisms which may facilitate this transition.
ISSN: 1553-734X EISSN: 1553-7358 PLoS Computational Biology https://hal.archives-ouvertes.fr/hal-01314721 PLoS Computational Biology, Public Library of Science, 2016, 12 (5)ARRAY(0x7f5470da9560) 2016-05-05
oai:hal.archives-ouvertes.fr:hal-01314723
Evolution of Cooperation in Evolutionary Robotics : the Tradeoff between Evolvability and Efficiency
International audience
In this paper, we investigate the benefits and drawbacks of different approaches for solving a cooperative foraging task with two robots. We compare a classical clonal approach with an additional approach which favors the evolution of heterogeneous behaviors according to two defining criteria: the evolvability of the cooperative solution and the efficiency of the coordination behaviors evolved. Our results reveal a tradeoff between evolvability and efficiency: the clonal approach evolves cooperation with a higher probability than a non-clonal approach, but heterogeneous behaviors evolved with the non-clonal approach systematically show better fitness scores. We then propose to overcome this tradeoff and improve on both of these criteria for each approach. To this end, we investigate the use of incremental evolution to transfer coordination behaviors evolved in a simpler task. We show that this leads to a significant increase in evolvability for the non-clonal approach, while the clonal approach does not benefit from any gain in terms of efficiency.
Proceedings of the European Converence on Artificial Life 2015 https://hal.archives-ouvertes.fr/hal-01314723 Proceedings of the European Converence on Artificial Life 2015, Jul 2015, York, United Kingdom. pp.195-502, 2015, <10.7551/978-0-262-33027-5-ch087>ARRAY(0x7f5470da9218) 2015-07-20
Soutenance
Thèse: Les Mécanismes de la Coordination et l'Evolution de la Coopération : de la Modélisation Computationnelle à la Conception en Robotique Evolutionniste
Soutenance: 2016-11-28
Rapporteurs: Guillaume BESLON    Richard WATSON